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Semi-supervised classification based on subspace sparse representation

Yu, Guoxian, Zhang, Guoji, Zhang, Zili, Yu, Zhiwen and Deng, Lin 2015, Semi-supervised classification based on subspace sparse representation, Knowledge and information systems, vol. 43, no. 1, pp. 81-101, doi: 10.1007/s10115-013-0702-2.

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Title Semi-supervised classification based on subspace sparse representation
Author(s) Yu, Guoxian
Zhang, Guoji
Zhang, ZiliORCID iD for Zhang, Zili
Yu, Zhiwen
Deng, Lin
Journal name Knowledge and information systems
Volume number 43
Issue number 1
Start page 81
End page 101
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-04
ISSN 0219-1377
Keyword(s) Graph construction
High-dimensional data
Semi-supervised classification
Subspaces sparse representation
Summary Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters.
Language eng
DOI 10.1007/s10115-013-0702-2
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, Springer
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Document type: Journal Article
Collection: School of Information Technology
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